In recent years much research has been dedicated to applying deep learning (\(\mathsf {DL}\) ) techniques for designing and optimizing wireless communication networks, e.g. [1]}, [2]}, [3]}, [4]}. Several papers propose \(\mathsf {DL}\)  methods to handle \(\mathsf {PAPR}\)  reduction. For example, the authors of [5]}, [6]}, added a
neural network (\(\mathsf {NN}\) )
to reduce the complexity of the active constellation
scheme, followed by \(\mathsf {CF}\) .
In [7]}, [8]} the authors present an autoencoder (\(\mathsf {AE}\) ) solution for \(\mathsf {PAPR}\)  reduction, while minimizing the \(\mathsf {BER}\)  degradation.
The authors in [9]}, [10]} proposed a deep \(\mathsf {NN}\)  combined with \(\mathsf {SLM}\)  to mitigate the high \(\mathsf {PAPR}\)  issue of OFDM signal types. They use an \(\mathsf {AE}\)  structure to represent the constellation mapping and de-mapping of the transmitted symbols.
